 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 chr22-3pop.vcf.gz                                                
 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 +                                                                +
 +   POPULATION SIZE, MIGRATION, DIVERGENCE, ASSIGNMENT, HISTORY  +
 +   Bayesian inference using the structured coalescent           +
 +                                                                +
 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
  Compiled for a PARALLEL COMPUTER ARCHITECTURE
  One master and 5 compute nodes are available.
  PDF output enabled [Letter-size]
  Version 6.0.1 [Mittag (merged with main Oct 11 2025)]   [October-11-2025]
  Program started at   Tue Jan  6 18:47:34 2026
         finished at Tue Jan  6 18:48:18 2026
                          


Options in use:
---------------

Analysis strategy is BAYESIAN INFERENCE
    - Population size estimation: Theta [Exponential Distribution]
    - Geneflow estimation: Migration [Exponential Distribution]

Proposal distribution:
Parameter group          Proposal type
-----------------------  -------------------
Population size (Theta)  Metropolis sampling
Migration rate      (M)  Metropolis sampling
Divergence Time (D)  Metropolis sampling
Divergence time spread (STD) Metropolis sampling
Genealogy                Metropolis-Hastings


Prior distribution (Proposal-delta will be tuned to acceptance frequency 0.440000):
Parameter group            Prior type   Minimum    Mean(*)    Maximum    Delta      Bins   Updatefreq
-------------------------  ------------ ---------- ---------- ---------- ---------- ------ -------
Population size (Theta_1)   Exponential    0.000000   0.100000   0.200000           -   1500  0.05556
Population size (Theta_2)   Exponential    0.000000   0.100000   0.200000           -   1500  0.05556
Population size (Theta_3)   Exponential    0.000000   0.100000   0.200000           -   1500  0.05556
Migration 2 to 1   (M)      Exponential    0.000000  100.000000 10000.0000          -   1500  0.05556
Migration 3 to 1   (M)      Exponential    0.000000  100.000000 10000.0000          -   1500  0.05556
Migration 1 to 2   (M)      Exponential    0.000000  100.000000 10000.0000          -   1500  0.05556
Migration 3 to 2   (M)      Exponential    0.000000  100.000000 10000.0000          -   1500  0.05556
Migration 1 to 3   (M)      Exponential    0.000000  100.000000 10000.0000          -   1500  0.05556
Migration 2 to 3   (M)      Exponential    0.000000  100.000000 10000.0000          -   1500  0.05556
Datatype: DNA sequence data

Inheritance multipliers in use for Thetas (specified # 1)
All inheritance multipliers are the same [1.000000]

Pseudo-random number generator: Mersenne-Twister                                
Random number seed (with internal timer)           3486927686

Start parameters:
   First genealogy was started using a random tree
   Start parameter values were generated
Connection matrix:
m = average (average over a group of Thetas or M,
s = symmetric migration M, S = symmetric 4Nm,
0 = zero, and not estimated,
* = migration free to vary, Thetas are on diagonal
d = row population split off column population
D = split and then migration
   1 Pop1           * * * 
   2 Pop2           * * * 
   3 Pop3           * * * 



Mutation rate is constant for all loci

Markov chain settings:
   Long chains (long-chains):                              1
      Steps sampled (long-inc*samples):              1000000
      Steps recorded (long-sample):                    10000
   Static heating scheme
      4 chains with  temperatures
       1.00, 1.50, 3.00,1000000.00
      Swapping interval is 1
   Burn-in per replicate (samples*inc):               100000

Print options:
   Data file:                                         infile
   Parameter file:                     parmfile-xxxxxxxxx-5e
   Haplotyping is turned on:                              NO
   Output file (ASCII text):            outfile-xxxxxxxxx-5e
   Output file (PDF):               outfile-xxxxxxxxx-5e.pdf
   Print data:                                            No
   Print genealogies:                                     No



Bayesian estimates
==================

Locus Parameter        2.5%      25.0%    mode     75.0%   97.5%     median   mean
-----------------------------------------------------------------------------------
    1  Theta_1         0.00000  0.00107  0.00353  0.00560  0.01520  0.00473  0.00415
    1  Theta_2         0.00000  0.00093  0.00327  0.00547  0.02947  0.00460  0.00588
    1  Theta_3         0.00000  0.00093  0.00287  0.00467  0.00920  0.00380  0.00214
    1  M_2->1           0.0000  60.0000 176.6667 280.0000 553.3333 230.0000 156.7517
    1  M_3->1           0.0000  73.3333 203.3333 320.0000 633.3333 263.3333 204.2138
    1  M_1->2           0.0000  53.3333 156.6667 253.3333 493.3333 203.3333 113.6787
    1  M_3->2           0.0000  73.3333 203.3333 326.6667 673.3333 270.0000 213.3929
    1  M_1->3           0.0000  53.3333 163.3333 260.0000 520.0000 210.0000 127.1357
    1  M_2->3           0.0000  80.0000 203.3333 320.0000 613.3333 256.6667 204.3031
    2  Theta_1         0.00000  0.00093  0.00273  0.00453  0.00867  0.00367  0.00178
    2  Theta_2         0.00000  0.00080  0.00300  0.00493  0.01520  0.00420  0.00365
    2  Theta_3         0.00000  0.00093  0.00273  0.00453  0.00867  0.00367  0.00186
    2  M_2->1           0.0000  53.3333 156.6667 253.3333 493.3333 203.3333 115.7523
    2  M_3->1           0.0000  53.3333 150.0000 246.6667 480.0000 203.3333 107.1458
    2  M_1->2           0.0000  60.0000 170.0000 266.6667 520.0000 216.6667 135.1497
    2  M_3->2           0.0000  66.6667 190.0000 300.0000 606.6667 243.3333 181.8212
    2  M_1->3           0.0000  53.3333 170.0000 260.0000 513.3333 216.6667 132.1057
    2  M_2->3           0.0000  53.3333 170.0000 266.6667 533.3333 223.3333 140.1949
    3  Theta_1         0.00000  0.00093  0.00300  0.00493  0.01173  0.00407  0.00279
    3  Theta_2         0.00000  0.00080  0.00273  0.00453  0.00987  0.00380  0.00222
    3  Theta_3         0.00000  0.00080  0.00273  0.00453  0.00973  0.00380  0.00208
    3  M_2->1           0.0000  53.3333 163.3333 260.0000 533.3333 216.6667 129.6216
    3  M_3->1           0.0000  53.3333 163.3333 260.0000 526.6667 216.6667 131.1568
    3  M_1->2           0.0000  53.3333 156.6667 253.3333 500.0000 210.0000 119.7188
    3  M_3->2           0.0000  53.3333 163.3333 260.0000 520.0000 216.6667 128.8990
    3  M_1->3           0.0000  53.3333 163.3333 253.3333 506.6667 210.0000 122.3741
    3  M_2->3           0.0000  53.3333 163.3333 260.0000 520.0000 210.0000 127.8660
    4  Theta_1         0.00000  0.00080  0.00273  0.00440  0.00867  0.00367  0.00178
    4  Theta_2         0.00000  0.00080  0.00260  0.00427  0.00867  0.00353  0.00165
    4  Theta_3         0.00000  0.00080  0.00313  0.00507  0.02160  0.00447  0.00479
    4  M_2->1           0.0000  53.3333 163.3333 260.0000 533.3333 216.6667 134.7054
    4  M_3->1           0.0000  53.3333 156.6667 253.3333 493.3333 203.3333 114.7359
    4  M_1->2           0.0000  53.3333 163.3333 253.3333 500.0000 210.0000 120.9049
    4  M_3->2           0.0000  53.3333 156.6667 246.6667 486.6667 203.3333 109.3254
    4  M_1->3           0.0000  80.0000 203.3333 320.0000 626.6667 263.3333 208.6667
    4  M_2->3           0.0000  53.3333 170.0000 273.3333 573.3333 223.3333 149.8133
    5  Theta_1         0.00000  0.00093  0.00313  0.00507  0.01320  0.00433  0.00334
    5  Theta_2         0.00000  0.00000  0.00433  0.01027  0.12053  0.01033  0.02440
    5  Theta_3         0.00000  0.00080  0.00273  0.00440  0.00840  0.00367  0.00171
    5  M_2->1           0.0000  46.6667 150.0000 233.3333 466.6667 196.6667  97.2696
    5  M_3->1           0.0000  60.0000 170.0000 273.3333 546.6667 223.3333 145.7881
    5  M_1->2           0.0000  60.0000 176.6667 280.0000 566.6667 230.0000 152.8720
    5  M_3->2           0.0000 120.0000 256.6667 433.3333 793.3333 343.3333 313.6271
    5  M_1->3           0.0000  53.3333 163.3333 253.3333 500.0000 210.0000 121.4429
    5  M_2->3           0.0000  46.6667 150.0000 233.3333 460.0000 196.6667  95.2379
  All  Theta_1         0.00000  0.00067  0.00220  0.00373  0.00680  0.00313  0.00225
  All  Theta_2         0.00000  0.00067  0.00233  0.00387  0.00680  0.00313  0.00233
  All  Theta_3         0.00000  0.00040  0.00207  0.00347  0.00653  0.00300  0.00204
  All  M_2->1          53.3333 240.0000 376.6667 506.6667 760.0000 403.3333 402.8108
  All  M_3->1          93.3333 300.0000 436.6667 600.0000 866.6667 476.6667 476.1081
  All  M_1->2          53.3333 246.6667 376.6667 506.6667 753.3333 403.3333 401.5481
  All  M_3->2         286.6667 533.3333 656.6667 780.0000 966.6667 650.0000 635.7368
  All  M_1->3          80.0000 280.0000 410.0000 540.0000 786.6667 430.0000 431.8483
  All  M_2->3         100.0000 300.0000 436.6667 573.3333 820.0000 456.6667 455.7439
-----------------------------------------------------------------------------------



Log-Probability of the data given the model (marginal likelihood = log(P(D|thisModel))
--------------------------------------------------------------------
[Use this value for Bayes factor calculations:
BF = Exp[log(P(D|thisModel) - log(P(D|otherModel)]
shows the support for thisModel]



Locus          TI(1a)       BTI(1b)         HS(2)
-------------------------------------------------
      1      -1576.01      -1460.74      -1430.18
      2      -1543.99      -1430.49      -1404.62
      3      -1533.61      -1425.17      -1406.07
      4      -1534.31      -1425.43      -1401.76
      5      -1554.85      -1448.11      -1423.19
---------------------------------------------------------------
  All        -7701.29      -7148.45      -7075.95
[Scaling factor = 41.480477]


(1a) TI: Thermodynamic integration: log(Prob(D|Model)): Good approximation with many temperatures
(1b) BTI: Bezier-approximated Thermodynamic integration: when using few temperatures USE THIS!
(2)  HS: Harmonic mean approximation: Overestimates the marginal likelihood, poor variance



MCMC run characteristics
========================




Acceptance ratios for all parameters and the genealogies
---------------------------------------------------------------------

Parameter           Accepted changes               Ratio
Theta_1                  11101/278089            0.03992
Theta_2                  27351/277688            0.09850
Theta_3                   9535/278785            0.03420
M_2->1                  191597/277341            0.69084
M_3->1                  182592/278129            0.65650
M_1->2                  193650/278038            0.69649
M_3->2                  155122/277698            0.55860
M_1->3                  180021/277100            0.64966
M_2->3                  179533/278273            0.64517
Genealogies            1277048/2498859           0.51105



Autocorrelation for all parameters and the genealogies
-------------------------------------------------------------------

Parameter           Autocorrelation           Effective Sample size
Theta_1                   0.543                 15015.241
Theta_2                   0.478                 17779.659
Theta_3                   0.422                 20370.729
M_2->1                    0.363                 23636.871
M_3->1                    0.415                 21176.306
M_1->2                    0.358                 23963.722
M_3->2                    0.528                 16919.439
M_1->3                    0.377                 22922.269
M_2->3                    0.404                 21720.258
Genealogies               0.598                 12602.826
(*) averaged over loci.



Temperatures during the run using the standard heating scheme
===========================================================================

Chain Temperature               log(marginal likelihood)  log(mL_steppingstone)
    1    1.00000          -1413.16279  -1181.95539
    2    1.00000          -1418.72245  -798.77711
    3    1.00000          -1447.04452  -411.17212
    4    1.00000          -2146.63905   -12.94459
